Social Security Benefit Valuation, Risk, and Optimal Retirement

29 Pages Posted: 16 Aug 2019 Last revised: 2 Sep 2019

See all articles by Yassmin Ali

Yassmin Ali

New Jersey Institute of Technology - Martin Tuchman School of Management

Pablo Andreas Arrutia Sota

New Jersey Institute of Technology - Martin Tuchman School of Management

Ming Taylor

New Jersey Institute of Technology

Stephen Michael Taylor

New Jersey Institute of Technology

Xun Wang

New Jersey Institute of Technology - Martin Tuchman School of Management

Date Written: August 13, 2019

Abstract

We develop techniques to estimate the present day value of the future social security benefits of a retiree based upon their chosen date of retirement, the term structure of interest rates, and life expectancy forecasts. These valuation methods are then used to determine the optimal retirement time of a beneficiary given a specific wage history and health profile in the sense of maximizing the present day value of future cashflows. We then examine how a number of risk factors including interest rates, disease diagnosis, and population life table risks impact the current value of future payments. Specifically, we utilize principal component analysis in order to assess interest rate and population life expectancy variation risks. We then examine how such risks range over distinct income and demographic groups and finally summarize future research directions.

Keywords: social security, principal component analysis, pension risk

Suggested Citation

Ali, Yassmin and Sota, Pablo and Taylor, Ming and Taylor, Stephen Michael and Wang, Xun, Social Security Benefit Valuation, Risk, and Optimal Retirement (August 13, 2019). Available at SSRN: https://ssrn.com/abstract=3438080 or http://dx.doi.org/10.2139/ssrn.3438080

Yassmin Ali

New Jersey Institute of Technology - Martin Tuchman School of Management ( email )

United States

Pablo Sota

New Jersey Institute of Technology - Martin Tuchman School of Management ( email )

United States

Ming Taylor

New Jersey Institute of Technology ( email )

University Heights
Newark, NJ 07102
United States

Stephen Michael Taylor (Contact Author)

New Jersey Institute of Technology ( email )

University Heights
Newark, NJ 07102
United States

Xun Wang

New Jersey Institute of Technology - Martin Tuchman School of Management ( email )

United States

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